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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 56 Documents
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Search results for , issue "Vol 40, No 2: November 2025" : 56 Documents clear
Improving recommendations with implicit trust propagation from ratings and check-ins Medjroud, Sara; Dennouni, Nassim; Loukam, Mourad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp814-828

Abstract

This paper investigates how the propagation of implicit trust between users affects the quality of point-of-interest (POI) recommendations in location-based social networks (LBSNs). Through the analysis of user interactions via ratings and check-ins, this work proposes a recommendation model known as propagation of rating/check-in for implicit trust (PRCT). This model relies on two primary approaches: Similarity trust rating (STR), which utilizes user ratings, and similarity trust check-in (STC), which focuses on check-ins data. Both approaches employ trust propagation to enhance their similarity matrices between users. An evaluation of the PRCT model using the Yelp dataset shows that the STR approach surpasses other variants in terms of PRECISION and RECALL, while the STC approach demonstrates superior performance in terms of RMSE. Furthermore, while trust propagation in the PRCT model increases the density of its similarity matrices, it does not consistently enhance its PRECISION parameter. Only the similarity Jaccard check-in (SJC) and similarity cosine check-in (SCC) approaches show a significant improvement of this parameter. 
Interpretable federated deep learning models for predicting gait dynamics in biomechanics Ahamed, Shaik Sayeed; Pasha, Akram; Rahman, Syed Ziaur; Kumar, D. N. Puneeth
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1087-1099

Abstract

Accurate prediction of human joint angle dynamics and reliable gait classifica tion are essential for applications in rehabilitation, biomechanics, and clinical monitoring. Traditional machine learning (ML) models trained on centralized data raise concerns about privacy, scalability, and transparency. This study proposes a federated deep learning (DL) framework that integrates privacy preserving model training with interpretable predictions. Specifically, a gated recurrent unit- deep neural network (GRU-DNN) hybrid model is developed for regression of joint angles, while a Long short-term memory- convolutional neural network (LSTM-CNN) hybrid model is designed for binary and multi class gait classification. The framework is deployed using the federated av eraging (FedAvg) algorithm across simulated clients, with each client training locally on its data. To enhance interpretability, the local interpretable model agnostic explanations (LIME) algorithm is integrated at the client level to gener ate human-understandable explanations for model predictions. The experimen tal results demonstrate significant improvements, including a reduction in global mean squared error (GMSE) from 56.16 to 3.31 and an increase in R-squared score from 0.80 to 0.99 for regression, along with classification accuracies of 0.97 (binary) and 0.94 (multi-class). This scalable, privacy-preserving frame work bridges the gap between accuracy and transparency, offering impactful applications in biomechanics, healthcare, and personalized medicine.
End-to-end system for translating bahasa isyarat Indonesia sign language gestures into Indonesian text Putra, Satria; Rakun, Erdefi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp719-734

Abstract

This study addresses critical challenges in developing an end-to-end bahasa isyarat Indonesia (BISINDO) SLT by integrating advanced deep learning techniques to overcome complex background interference, transitional gesture recognition, and limitations in dataset availability. While existing SLT systems struggle with isolated word recognition and manual preprocessing, our work introduces three key innovations: (1) implementation of YOLOv8 for optimized object detection, achieving 88% mAP and reducing WER to 11.40%, outperforming YOLOv5/v7 in handling complex backgrounds; (2) automated removal of transitional gestures using Threshold conditional random fields (TCRF), which attained 95.68% accuracy, significantly improving upon MobileNetV2’s performance (WER: 6.89% vs. 93.53%); and (3) end-to-end BISINDO SLT by expansion of the BISINDO dataset to 435 word labels, enabling comprehensive sentencelevel translation. Experimental results demonstrate the system’s robustness, with 8.31% of WER, 84.13% of SAcc, and 87.08% of SacreBLEU after dataset expansion and redundancy elimination through grouping methods. The proposed framework operates without manual intervention, marking a substantial advancement toward real-world applicability.
Boosting carbon removal efficiency in wastewater treatment systems using a fuzzy model predictive control stategy Dhouibi, Saïda; Jarray, Raja; Bouallègue, Soufiene
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp629-639

Abstract

The efficient removal of carbon pollution has always presented a growing challenge facing wastewater treatment plants (WWTPs) operating with activated sludge process (ASP) technology. Enhancing pollution removal efficiency to meet standard wastewater quality limits remains a problematic in water pollution management. Recent progress in modeling and automatic control techniques can significantly improve the hydric pollution removal. In this paper, an effective carbon elimination strategy combining TakagiSugeno (TS) fuzzy modeling and model predictive control (MPC) is proposed to achieve high purification performance in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended solids (TSS) indicators. A fuzzy TS model is established based on the concepts of quasi-linear parameter-varying (LPV) forms and convex polytopic transformations of the system nonlinearities. The concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen as well as the effluent volume are controlled and maintained around their desired references with the aim of increasing pollution removal. Comparisons with the previously most used state-of-the-art parallel distributed compensation (PDC) are performed. High and competitive pollution removal percentages of 91% for COD and BOD5 indicators, and 92% for TSS metric, are achieved with the proposed MPC-based design, thus complying with the normative limits defined in WWTPs.
Unveiling educational enrollment factors in Egypt via ensemble learning Alsheref, Fahad kamal; Mostafa El Misery, Mostafa Sayed; Bahloul, Mahmoud Mohamed; Magdi, Dalia A.; Fattoh, Ibrahim Eldesouky
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp941-952

Abstract

Education plays a vital role in the development of a nation and significantly influences the direction of societies. Understanding the various factors that impact educational enrollment is essential for policymakers and resource allocation strategies. This paper explores the factors impacting educational enrollment in Egypt using predictive modeling and machine learning techniques. The study evaluates six machine learning algorithms and ensemble learning approaches to predict enrollment rates, considering computational efficiency, robustness, and parameter sensitivity. By analyzing socio-economic and demographic indicators from Egyptian educational data, the research examines the interplay of these factors. Results highlight the effectiveness of these methods in elucidating enrollment patterns, with ensemble learning showing promising performance and significant improvements compared to traditional machine learning algorithms. This study offers insights into Egypt's educational landscape that could inform policy formulation and resource allocation strategies.
Fuzzy multi-objective energy optimization of workflow scheduling Chehlaf, Ayoub; Gabli, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp871-882

Abstract

Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.
Optimizing clustering efficiency with weighted k-means: a machine learning-driven approach for enhanced accuracy and scalability Kaushik, Vishal; Aleem, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1121-1128

Abstract

Data analysis unlocks the hidden, latent patterns and structures within datasets. Clustering algorithms, the cornerstone of any data analysis, are usually challenged by high-dimensionality, complexity, or large-scale data. This research proposes a hybrid model that merges neural networks and clustering techniques to handle these problems. Neural networks are used for feature extraction and dimensionality reduction; raw data will be transformed into a robust, low-dimensional representation. With these refined features, the performance of clustering algorithms improves in terms of scalability, efficiency, and accuracy. The proposed model is tested on diversified datasets such as the wisconsin breast cancer dataset (WBCD), GEO Dataset, and image and text data benchmarks for which substantial improvements in clustering metrics such as silhouette score, purity, and computational efficiency are reported. The results demonstrate the efficacy of the hybrid approach in optimizing clustering applications across domains, such as bioinformatics, health care, and image analysis.
Optimizing social issue sentiment analysis with hybrid Chi-square and bayesian-optimized binary coordinate ascent Abiera Atillo, Guilbert Nicanor; Cardeno, Ralph Alanunay
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp772-779

Abstract

Feature selection aims to reduce the dimensionality of the feature space and prevent overfitting. However, when striving to produce accurate models for sentiment classification, feature selection introduces several challenges, particularly concerning textual content. Consequently, many researchers are exploring hybrid feature selection methods to customize the selection process and develop more advanced automated techniques, recognizing that the performance of these methods depends on hyperparameters. Integrating Bayesian Optimization into binary coordinate ascent (BCA) enhances the search for optimal solutions and improves classification performance in sentiment analysis, explicitly focusing on classifying abortion sentiment using Naïve Bayes. The effectiveness of combining Chi2 feature selection with the hybridized BCA and Bayesian Optimization approach is tested across multiple n-gram configurations. Results demonstrate significant improvements in accuracy and recall compared to Chi2 and BCA hybrid methods. For instance, the Bayesian Optimization-enhanced approach achieved up to 93.80% accuracy (1-gram) and 100% recall (4-gram), outperforming the baseline method. The study highlights trade-offs between computational efficiency and performance, noting that while the Chi2 and BCA hybrid method has lower training time complexity, the Bayesian Optimization-enhanced method excels in accuracy and recall during testing. The findings suggest that integrating Bayesian Optimization into feature selection improves sentiment classification performance and recommend further exploration of this approach with other classification algorithms, especially for social issues like abortion sentiment analysis.
Blockchain-based handle-research data sharing: a blockchain-based handle system to enhance the privacy and security of research data sharing Hisseine, Mahamat Ali; Chen, Deji; Xiao, Yang
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1065-1086

Abstract

The increasing demand for secure, persistent and interoperable research data (RD) sharing makes traditional systems vulnerable. All research objects should be findable, accessible, interoperable and reusable (FAIR) for machines and people. This paper proposes a novel framework called blockchain-based handle- RD sharing (BHRDS), which integrates the handle system for persistent identifiers (PIDs) with a smart contract for access control and mirror-specific encryption, BLAKE2-based hashing for identity binding and irregularity detection. The system utilizes swarm, a decentralized storage layer, for off-chain data storage while storing only credential metadata and access conditions on-chain. The framework enables secure identity data management, and verifiable credential distribution across multiple mirror sites. We conducted experiments under growing user numbers (10 to 10,000), different encryption key strengths (AES 128, 192, and 256 bits), and blockchain load conditions. Results show that BHRDS achieves high irregularity detection rates (above 97%) and maintains low response times even at scale. In all the test instances, the system performed accurately, demonstrating that BHRDS offers a decentralized data access model that is scalable and aligned with the FAIR principle, making it suitable for next-generation scientific and institutional data sharing. 
Enhancing the ternary neural networks with adaptive threshold quantization Truong, Son Ngoc
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp700-706

Abstract

Ternary neural networks (TNNs) with weights constrained to –1, 0, and +1 offer an efficient deep learning solution for low-cost computing platforms such as embedded systems and edge computing devices. These weights are typically obtained by quantizing the real weight during the training process. In this work, we propose an adaptive threshold quantization method that dynamically adjusts the threshold based on the mean of weight distribution. Unlike fixed-threshold approaches, our method recalculates the quantization threshold at each training epoch according to the distribution of real valued synaptic weights. This adaptation significantly enhances both training speed and model accuracy. Experimental results on the MNIST dataset demonstrates a 2.5× reduction in training time compared to conventional methods, with a 2% improvement in recognition accuracy. On Google Speech Command dataset, the proposed method achieves an 8% improvement in recognition accuracy and a 50% reduction in training time, compared to fixed-threshold quantization. These results highlight the effectiveness of adaptive quantization in improving the efficiency of TNNs, making them well-suited for deployment on resource constrained edge devices.

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